initial: import DOLPHIN baseline 2026-04-21 from dolphinng5_predict working tree
Includes core prod + GREEN/BLUE subsystems: - prod/ (BLUE harness, configs, scripts, docs) - nautilus_dolphin/ (GREEN Nautilus-native impl + dvae/ preserved) - adaptive_exit/ (AEM engine + models/bucket_assignments.pkl) - Observability/ (EsoF advisor, TUI, dashboards) - external_factors/ (EsoF producer) - mc_forewarning_qlabs_fork/ (MC regime/envelope) Excludes runtime caches, logs, backups, and reproducible artifacts per .gitignore.
This commit is contained in:
hjnormey
153
nautilus_dolphin/test_edge_signal_5y.py
Executable file
153
nautilus_dolphin/test_edge_signal_5y.py
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"""Fast Vectorized Edge Signal Existence Test — 5-Year Klines
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=============================================================
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Tests four hypotheses simultaneously:
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A. vel_div <= -0.02 SHORT (original system)
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B. vel_div <= -0.02 LONG (inverse: signal predicts UP)
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C. vel_div >= +0.02 SHORT (opposite sign as SHORT trigger)
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D. vel_div >= +0.02 LONG (opposite sign predicts UP)
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Compares each against unconditional 120-bar baseline.
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No engine stack — raw signal only. ~1-3 min runtime.
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"""
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import sys, time
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sys.stdout.reconfigure(encoding='utf-8', errors='replace')
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from pathlib import Path
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import numpy as np
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import pandas as pd
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from collections import defaultdict
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VBT_DIR = Path(r"C:\Users\Lenovo\Documents\- DOLPHIN NG HD HCM TSF Predict\vbt_cache_klines")
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TP_BPS = 95
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MAX_HOLD = 120
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tp_pct = TP_BPS / 10000.0
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parquet_files = sorted(VBT_DIR.glob("*.parquet"))
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parquet_files = [p for p in parquet_files if 'catalog' not in str(p)]
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total = len(parquet_files)
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print(f"Files: {total} TP={TP_BPS}bps MAX_HOLD={MAX_HOLD} bars\n")
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# Four buckets: (signal_mask_fn, direction)
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# direction: -1=SHORT (price must fall tp_pct), +1=LONG (price must rise tp_pct)
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TESTS = [
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('A: vd<=-0.02 SHORT', lambda vd: vd <= -0.02, -1),
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('B: vd<=-0.02 LONG', lambda vd: vd <= -0.02, +1),
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('C: vd>=+0.02 SHORT', lambda vd: vd >= +0.02, -1),
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('D: vd>=+0.02 LONG', lambda vd: vd >= +0.02, +1),
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]
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# Per-year per-test stats
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stats = {label: defaultdict(lambda: {
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'total_bars': 0, 'signal_bars': 0,
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'wins': 0, 'losses': 0,
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'loss_returns': [],
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}) for label, _, _ in TESTS}
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ctrl = defaultdict(lambda: {'up': 0, 'dn': 0, 'n': 0}) # control per year
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t0 = time.time()
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for i, pf in enumerate(parquet_files):
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ds = pf.stem
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year = ds[:4]
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try:
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df = pd.read_parquet(pf)
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except Exception:
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continue
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if 'vel_div' not in df.columns or 'BTCUSDT' not in df.columns:
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continue
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vd = np.where(np.isfinite(df['vel_div'].values), df['vel_div'].values, 0.0)
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btc = df['BTCUSDT'].values
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btc = np.where(np.isfinite(btc) & (btc > 0), btc, np.nan)
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n = len(vd)
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for label, mask_fn, direction in TESTS:
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ys = stats[label][year]
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ys['total_bars'] += n
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sig_idx = np.where(mask_fn(vd))[0]
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for j in sig_idx:
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ep = btc[j]
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if not np.isfinite(ep) or ep <= 0:
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continue
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end_j = min(j + MAX_HOLD, n - 1)
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fut = btc[j+1:end_j+1]
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fm = np.isfinite(fut)
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if not np.any(fm):
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continue
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fin = np.where(fm, fut, np.nan)
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if direction == -1: # SHORT: need price to fall
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tp_price = ep * (1.0 - tp_pct)
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hit = np.nanmin(fin) <= tp_price
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else: # LONG: need price to rise
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tp_price = ep * (1.0 + tp_pct)
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hit = np.nanmax(fin) >= tp_price
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if hit:
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ys['wins'] += 1
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else:
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last = fin[~np.isnan(fin)][-1]
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ret = (last - ep) / ep * direction # sign-adjusted
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ys['losses'] += 1
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ys['loss_returns'].append(ret)
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ys['signal_bars'] += 1
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# Control: unconditional every 60th bar
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cy = ctrl[year]
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for j in range(0, n - MAX_HOLD, 60):
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ep = btc[j]; ex = btc[j + MAX_HOLD]
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if np.isfinite(ep) and np.isfinite(ex) and ep > 0:
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ret = (ex - ep) / ep
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cy['up'] += int(ret >= tp_pct)
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cy['dn'] += int(ret <= -tp_pct)
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cy['n'] += 1
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if (i + 1) % 200 == 0:
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el = time.time() - t0
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print(f" [{i+1}/{total}] {ds} {el:.0f}s")
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elapsed = time.time() - t0
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# ── Print results ──────────────────────────────────────────────────────────────
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ctrl_dn_tot = sum(cy['dn'] for cy in ctrl.values())
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ctrl_up_tot = sum(cy['up'] for cy in ctrl.values())
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ctrl_n_tot = sum(cy['n'] for cy in ctrl.values())
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ctrl_dn_pct = ctrl_dn_tot / ctrl_n_tot * 100 if ctrl_n_tot else 0
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ctrl_up_pct = ctrl_up_tot / ctrl_n_tot * 100 if ctrl_n_tot else 0
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print(f"\nControl (unconditional 120-bar):")
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print(f" DOWN {TP_BPS}bps: {ctrl_dn_pct:.1f}% UP {TP_BPS}bps: {ctrl_up_pct:.1f}% n={ctrl_n_tot:,}")
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for label, _, direction in TESTS:
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all_yrs = stats[label]
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tot_sig = sum(y['signal_bars'] for y in all_yrs.values())
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tot_win = sum(y['wins'] for y in all_yrs.values())
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tot_los = sum(y['losses'] for y in all_yrs.values())
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tot_n = tot_win + tot_los
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wr = tot_win / tot_n * 100 if tot_n else 0
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gw = tot_win * tp_pct
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all_lr = [r for y in all_yrs.values() for r in y['loss_returns']]
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gl = abs(sum(all_lr)) if all_lr else 0
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pf = gw / gl if gl > 0 else 999
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ctrl_bl = ctrl_dn_pct if direction == -1 else ctrl_up_pct
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edge = wr - ctrl_bl
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print(f"\n{'─'*60}")
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print(f" {label}")
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print(f" {'Year':<6} {'Sigs':>8} {'WR':>7} {'PF':>7} {'Edge':>8}")
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print(f" {'─'*46}")
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for year in sorted(all_yrs.keys()):
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ys = all_yrs[year]
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yn = ys['wins'] + ys['losses']
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ywr = ys['wins'] / yn * 100 if yn else 0
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ygw = ys['wins'] * tp_pct
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ygl = abs(sum(ys['loss_returns'])) if ys['loss_returns'] else 0
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ypf = ygw / ygl if ygl > 0 else 999
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yedge = ywr - ctrl_bl
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print(f" {year:<6} {ys['signal_bars']:>8,} {ywr:>6.1f}% {ypf:>7.3f} {yedge:>+7.1f}pp")
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print(f" {'─'*46}")
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print(f" {'TOTAL':<6} {tot_sig:>8,} {wr:>6.1f}% {pf:>7.3f} {edge:>+7.1f}pp ← {'EDGE' if edge > 2 else 'WEAK' if edge > 0 else 'NO EDGE'}")
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print(f"\n{'='*60}")
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print(f" Runtime: {elapsed:.0f}s")
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print(f"{'='*60}")
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